CN116824314A - Information acquisition methods and systems - Google Patents

Abstract

The invention provides an information acquisition method and system, which can be applied to the field of payment security, wherein the method comprises the following steps: pre-training the convolutional neural network by using a first training sample to obtain an image recognition model; performing optimization training on the image recognition model by using a second training sample to obtain a target image recognition model; carrying out face verification on the received identity verification information uploaded by the user terminal, and carrying out position verification on the identity verification information based on a target image recognition model; and under the condition that the face verification and the position verification are both passed, transmitting the received data information corresponding to the information acquisition request transmitted by the user terminal to the user terminal. The system is used for executing the method. The invention can realize double security guarantee of user identity and user environment through double identity authentication of face authentication and position authentication, and avoid property loss of users caused by authentication of user identity based on image processing means of some illegal user terminals.

Description

Information acquisition methods and systems

Technical field

The present invention relates to the field of information processing technology, and in particular to an information acquisition method and system.

Background technique

In the existing technology, the face recognition method mainly obtains the user's facial features alone, and then matches the facial features with the features in the feature library to achieve identity recognition. This method can only determine the user's identity, but cannot identify the user. The security of user environment information requires illegal user terminals to pass user identity verification based on image processing methods, thereby causing user property losses. It can be seen that how to eliminate security issues in the consumption or payment environment has become an urgent technical issue that needs to be solved.

Contents of the invention

The information acquisition method and system provided by the present invention are used to solve the problems existing in the existing technology. Through dual identity verification of face verification and location verification, dual security guarantees of user identity and user environment can be achieved, and some unscrupulous users can be avoided. The terminal, based on image processing means, causes the user's property loss through user identity verification.

An information acquisition method provided by the invention includes:

Pre-train the convolutional neural network using the first training sample to obtain the image recognition model;

Use the second training sample to optimize and train the image recognition model to obtain the target image recognition model;

Perform face verification on the received identity verification information uploaded by the user terminal and perform location verification on the identity verification information based on the target image recognition model;

If both the face verification and the location verification pass, send the data information corresponding to the received information acquisition request sent by the user terminal to the user terminal;

Wherein, the first training sample is obtained by labeling each environment image according to the first position information corresponding to each environment image in the plurality of acquired environment images;

The second training sample is determined based on the target image obtained by superimposing the face image and the environment image in the first training sample;

The identity verification information includes an environment image including the user's face image taken by the user in real time and the user's location information.

According to an information acquisition method provided by the present invention, pre-training a convolutional neural network using a first training sample to obtain an image recognition model includes:

The first training sample is input to the convolutional neural network for pre-training, and based on the comparison result between the second position information of each environmental image output by the convolutional neural network and the first position information, The hyperparameters of the convolutional neural network are adjusted;

According to the adjusted convolutional neural network, the image recognition model is determined.

According to an information acquisition method provided by the present invention, the optimization training of the image recognition model using the second training sample to obtain the target image recognition model includes:

The second training sample is input to the image recognition model for optimization training, and according to the comparison result of the third position information of the target image output by the image recognition model and the first position information, a reverse The propagation algorithm and the stochastic gradient algorithm optimize the image recognition model to obtain the target image recognition model.

According to an information acquisition method provided by the present invention, the location of the identity verification information uploaded by the user terminal is determined in the following manner:

After receiving the information acquisition request sent by the user terminal, sending identity verification information collection information to the user terminal, so that the user terminal collects information according to the received identity verification information, and collects the identity verify message.

According to an information acquisition method provided by the present invention, the face verification of the received identity verification information uploaded by the user terminal and the location verification of the identity verification information based on the target image recognition model include:

Extracting the first facial feature of the environment image including the user's facial image;

Perform feature vector calculation on the first facial feature and the second facial feature when the user registered in the identity verification information to obtain the distance between the first facial feature and the second facial feature ;

According to the distance, complete face verification of the identity verification information;

If the face verification passes, the identity verification information is location verified based on the target image recognition model.

According to an information acquisition method provided by the present invention, the location verification of the identity verification information based on the target image recognition model includes:

The environment image including the user's face image is input to the target image recognition model, and based on the comparison result between the fourth position information output by the target image recognition model and the user's position information, the identity is completed. Location verification of verification information.

The invention also provides an information acquisition system, including: a first training module, a second training module, an identity verification module and an information acquisition module;

The first training module is used to pre-train the convolutional neural network using the first training sample to obtain the image recognition model;

The second training module is used to optimize and train the image recognition model using the second training sample to obtain the target image recognition model;

The identity verification module is used to perform face verification on the received identity verification information uploaded by the user terminal and perform location verification on the identity verification information based on the target image recognition model;

The information acquisition module is configured to send data information corresponding to the received information acquisition request sent by the user terminal to the user terminal when both face verification and location verification pass;

Wherein, the first training sample is obtained by labeling each environment image according to the first position information corresponding to each environment image in the plurality of acquired environment images;

The second training sample is determined based on the target image obtained by superimposing the face image and the environment image in the first training sample;

The identity verification information includes an environment image including the user's face image taken by the user in real time and the user's location information.

The present invention also provides an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements any of the above information acquisition methods. .

The present invention also provides a processor-readable storage medium. The processor-readable storage medium stores a computer program. The computer program is used to cause the processor to execute any one of the above information acquisition methods.

The present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements any one of the above information acquisition methods.

The information acquisition method and system provided by the present invention can achieve dual security guarantees of user identity and user environment through dual identity verification of face verification and location verification, and avoid some unscrupulous user terminals, based on image processing means, through user identity verification. Verification causes property damage to the user.

Description of the drawings

In order to explain the present invention or the technical solutions in the prior art more clearly, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are of the present invention. For some embodiments of the invention, those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 is a schematic flow chart of the information acquisition method provided by the present invention;

Figure 2 is one of the structural schematic diagrams of the convolutional neural network provided by the present invention;

Figure 3 is the second structural schematic diagram of the convolutional neural network provided by the present invention;

Figure 4 is a schematic structural diagram of the Inception module in the convolutional neural network provided by the present invention;

Figure 5 is a schematic structural diagram of the information acquisition system provided by the present invention;

Figure 6 is a schematic diagram of the physical structure of the electronic device provided by the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention. , not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

When users enter an Internet application for the first time, they need to fill in personal information and bind a mobile phone number (send a SMS verification code) to register an Internet application account. During normal use, if sensitive operations such as payment and password retrieval are involved, now Some Internet applications usually use the method of verifying SMS verification codes and face recognition to identify the user's identity, so as to determine that it is operated by himself. This user identity verification method often cannot ensure the security of the user's payment environment. Based on this, the present invention provides a The information acquisition method adds multiple verification matching of environmental information, location information and facial expressions when the user performs identity recognition to achieve identity verification. The specific implementation is as follows:

Figure 1 is a schematic flow chart of the information acquisition method provided by the present invention. As shown in Figure 1, the method includes:

Step 100: Use the first training sample to pre-train the convolutional neural network to obtain an image recognition model;

Step 200: Use the second training sample to optimize and train the image recognition model to obtain the target image recognition model;

Step 300: Perform face verification on the received identity verification information uploaded by the user terminal and perform location verification on the identity verification information based on the target image recognition model;

Step 400: If both face verification and location verification pass, send the data information corresponding to the received information acquisition request sent by the user terminal to the user terminal;

Wherein, the first training sample is obtained by labeling each environment image according to the first position information corresponding to each environment image in the plurality of acquired environment images;

The second training sample is determined based on the target image obtained by superimposing the face image and the environment image in the first training sample;

The identity verification information includes the user's real-time environment image including the user's face image and the user's location information.

It should be noted that the execution subject of the above method may be a computer device.

Optionally, the present invention pre-labels the location information corresponding to each environment image in the plurality of collected environment images in the form of a location label to obtain the first training sample, and inputs the first training sample to Training and learning are performed in the convolutional neural network to obtain the image recognition model.

Environmental images refer to pictures including objects in natural scenes, which can specifically include pictures of buildings, street view pictures, indoor pictures of buildings, etc. When acquiring the environment image, the collection location information corresponding to the environment image (i.e., the first location information) will be obtained at the same time. The first training sample will be obtained after annotating the environment image through the first location information. For example, the annotation content can be **City** Street** Road.

Based on the image recognition model, the relationship between the environment image and the location information is mined; then the face image and the environment image in the first training sample are randomly superimposed and combined, and then input into the image recognition model to obtain the corresponding position of the face image and the environment image. Information, compare the location information with the location tag corresponding to the environment image, and optimize the image recognition model to obtain the target image recognition model.

The random superposition of the environment image of the face image in the second training sample means that after freely combining the face image and the environment image, the environment image is used as the background image and the face image is used as the foreground image for layer overlay.

When performing identity verification, face recognition is performed on the received identity verification information uploaded by the user terminal (including the environmental image including the user's face image and the user's location information taken in real time by the user), and the user is completed based on the face recognition results. Face verification of the identity verification information uploaded by the terminal, and location verification of the identity verification information based on the trained target image recognition model.

When performing user identity verification, the user's identity verification information including the environment image of the user's face image and the user's location information is collected, and the environment image including the user's face image is input into the target image recognition model to obtain the predicted location information, and then Match the predicted location information with the collected user location information to complete the user's location verification, and combine it with the face verification results to finally achieve the user's location verification and face verification.

For example, the face verification of the user can be completed by matching the facial features of the environment image including the user's face image with the user's facial features pre-stored in the feature library.

When both face verification and location verification pass, the data information corresponding to the received information acquisition request sent by the user terminal is sent to the user terminal.

The information acquisition method provided by the present invention can achieve dual security guarantees of user identity and user environment through dual identity verification of face verification and location verification, and avoids the possibility of some unscrupulous user terminals to verify the user identity based on image processing means. Causing property damage to users.

Further, in one embodiment, step 100 may specifically include:

Step 1001: Input the first training sample to the convolutional neural network for pre-training, and based on the comparison result of the second position information and the first position information of each environmental image output by the convolutional neural network, perform the convolutional neural network's Hyperparameters are adjusted;

Step 1002: Determine the image recognition model based on the adjusted convolutional neural network.

Optionally, in step 1001, the obtained first training sample can be input into the convolutional neural network as shown in Figure 2 for pre-training. The convolutional neural network has a multi-layer network structure and is also a multi-layer non-linear network. Fully connected neural network.

Functionally speaking, the convolution layer and the pooling layer are mainly responsible for extracting the features of the image and completing the mapping of image features from low dimensions to high dimensions, while the fully connected layer completes the transformation of high-dimensional features into image categories.

Optionally, the present invention uses Recog-Net, a deep convolutional network based on GoogleNet, as the convolutional neural network. Its structure is shown in Figure 3 and Figure 4 below:

Among them, Figure 3 shows the overall structure of the convolutional neural network, and Figure 4 shows the structure diagram of the Inception module in Figure 3. In Figure 3, the input of the convolutional neural network is an RGB three-channel environment image with a size of 229*229*3, followed by three stages of convolution operations C1, C2, and C3, followed by the accompanying p1 pooling dimensionality reduction. and convolution of C4 and C5 to extract more abstract high-level features.

As shown in Figure 4, the core of the convolutional neural network is the Inception module. The entire inception module is composed of three convolution kernels of different scales: 1*1, 3*3 and 5*5. At the same time, it is stacked together with the pooling operation (3*3) (the sizes after convolution and pooling are the same, and the channels are added). On the one hand, it increases the width of the convolutional neural network, and on the other hand, it also increases the number of convolutional neural networks. Network fit to scale. A ReLU operation is performed after each convolutional layer. As a nonlinear activation function, the ReLU function can effectively fit the training state of the convolutional neural network.

In the pre-training stage, the prediction results of the convolutional neural network are compared with the first position information marked corresponding to the first training sample, and then the convolutional neural network is optimized based on the comparison results. Specifically, the convolutional neural network can be optimized by Optimize and adjust the hyperparameters to determine the adjusted hyperparameters, and obtain the image recognition model based on the adjusted convolutional neural network.

The information acquisition method provided by the present invention uses the first training sample to train the convolutional neural network to obtain the image recognition model, which lays the foundation for subsequent acquisition of the target recognition model based on the image recognition network and ultimately the verification of the user's location.

Further, in one embodiment, step 200 may specifically include:

Step 2001: Input the second training sample to the image recognition model for optimization training, and use the back propagation algorithm and the stochastic gradient algorithm to compare the third position information and the first position information of the target image output by the image recognition model. The image recognition model is optimized to obtain the target image recognition model.

Optionally, the second training sample is input into the image recognition model pre-trained in step 100, and the position data predicted by the image recognition model (ie, the third position information) is combined with the position of the environment image marked in the second training sample. information (consistent with the first position information), and based on the comparison results, a continuous backpropagation algorithm combined with a stochastic gradient descent algorithm that dynamically changes the learning rate is used to optimize the training of the image recognition model, and the optimized image recognition model is obtained, and The optimized image recognition model is used as the target image recognition model.

For example, the location recognition of the environment image in the present invention can be the location recognition of the building image. In order to make the trained target image recognition model have more accurate discrimination ability, the present invention uses the original building image as the first training The sample is pre-trained with a convolutional neural network so that the trained image recognition model can accurately determine the location of the building; then, the face image and the building image are randomly superimposed, and the resulting randomly superimposed image is used as the third Second training samples are used to perform secondary optimization training on the image recognition model obtained after pre-training, so that the target image recognition model obtained after two trainings can realize building images including face images based on the memory of the pre-trained image recognition model. Make accurate predictions of location.

The information acquisition method provided by the present invention uses the second training sample to train the image recognition model, which lays the foundation for subsequent determination of the target recognition model based on the trained image recognition network, and ultimately the verification of the user's location.

Further, in one embodiment, the location of the identity verification information uploaded by the user terminal in step 300 is determined in the following manner:

After receiving the information acquisition request sent by the user terminal, identity verification information collection information is sent to the user terminal, so that the user terminal collects information according to the identity verification information and collects identity verification information.

Optionally, in many Internet applications, such as WeChat, Alipay, Meituan, Didi, etc., especially mobile 5G messaging functions, implementing 5G messaging related application functions will involve user identity verification. Current identity verification is mainly achieved through face recognition, fingerprint recognition, etc.

The present invention proposes a new identity verification method that combines location identity verification with environmental images. When users use Internet applications, they will obtain target page information from the server by sending information acquisition requests, such as personal center pages, password payment pages, etc. In the information acquisition request, the address data of the target request information and the user's identity information (such as user terminal identification, user account or number, etc.) will be included.

The server sends identity verification information to the user terminal to collect information based on the user identity information included in the information acquisition request.

After receiving the identity verification information collection information, the user terminal captures the environment image including the user's face image and the user's location information through the collection device, and uploads it to the server as identity verification information. The server receives the identity verification information and obtains the environment image; At the same time, the identity verification information includes user identity information, such as user mobile phone number.

The collection device may specifically be a built-in camera that collects environmental images including user face images and user location information. It can also specifically collect the environment image including the user's face image and the user's location information through an external camera associated with the user terminal. For example, the user terminal can be connected to the image acquisition device through a connecting line or network. The image acquisition device collects the environment image including the user's face image and the user's location information through the camera, and will collect the environment image including the user's face image and the user's location information. The location information is transmitted to the user terminal. The camera can be a monocular camera, a binocular camera, a depth camera, a 3D (3 Dimensions, three-dimensional) camera, etc. The user terminal can collect images of living objects in real scenes, and can also collect existing images containing faces in real scenes, such as scans of ID documents, etc.

Among them, the user terminal can be, but is not limited to, various smart phones, tablets, laptops, desktop computers, portable wearable devices, smart speakers, etc. The server can be specifically a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or it can provide cloud services, cloud databases, cloud computing, cloud functions, storage, network services, and cloud services. Cloud servers for basic cloud computing services such as communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

The information acquisition method provided by the present invention realizes the dual face verification and location verification of the user by collecting the user's real-time environment image including the user's face image and the user's location information included in the identity verification information uploaded by the user terminal. Identity verification improves the security of verification.

Further, in one embodiment, step 400 may specifically include:

Step 4001: Extract the first facial feature of the environment image including the user's facial image;

Step 4002: Calculate the feature vector between the first facial feature and the second facial feature in the identity verification information when the user is registered to obtain the distance between the first facial feature and the second facial feature;

Step 4003: Complete the face verification of the identity verification information based on the distance;

Step 4004: If the face verification passes, perform location verification on the identity verification information based on the target image recognition model.

Optionally, according to the environment image including the user's face image in the identity verification information uploaded by the user terminal, the server extracts the facial features, that is, the first facial features, from the environment image through the image processing module.

Through the user identity information in the identity verification information, the facial features obtained when the user registered, that is, the second facial features, are obtained from the facial feature database.

Facial features are the inherent physiological characteristics of the human face, such as iris shape, positional relationship between facial organs (eyes, nose, mouth, ears, etc.), structure of facial organs (shape, size, etc.), skin texture, etc. .

Perform feature vector calculation on the first facial feature extracted from the environment image including the user's face image in the identity verification information uploaded by the user terminal and the second facial feature obtained from the face feature library. For example, the Mahalanobis distance can be used Obtain the distance between the first facial feature and the second facial feature, and complete the face verification of the environment image including the user's face image in the identity verification information. If the calculated distance is zero, the face verification is passed. .

When the face verification is passed, the location verification of the user's location information in the identity verification information is performed based on the target image recognition model.

The information acquisition method provided by the present invention realizes environmental image verification based on location information by predicting the location information corresponding to the environment image and comparing the predicted location information with the location information collected by the user, thereby improving the security of the verification.

Further, in one embodiment, step 4004 may specifically include:

Step 40041: Input the environment image including the user's face image to the target image recognition model, and complete the location verification of the identity verification information based on the comparison result between the fourth location information output by the target image recognition model and the user's location information.

Optionally, input the environment image including the user's face image to the target image recognition model, and compare the position information (ie, the fourth position information) of the environment image including the face features output by the target image recognition model to complete Location verification of the user's location information.

For example, the location comparison may specifically include comparing the collected user's location information G _j with the predicted fourth location information G _i . If the collected user's location information G _j is located in the geographical location of the predicted fourth location information G _i Within the interval planning range, the location verification can be passed. In the process of location verification, in addition to simple geographical interval planning, other technologies are used to ensure the authenticity of the collected user location information during the process of collecting the user's location information.

For example, by checking the difference between the user's real-time location on map software, WeChat, etc. and the user's location information in the identity verification information uploaded by the user terminal, the authenticity of the collected user's location information can be ensured.

For another example, blockchain technology can be introduced, and the server can be used as a blockchain node in the blockchain network, and the environmental images including the user's face image and the user's location information captured in real time by the user can be stored in the blockchain. and obtain the user's location information from the data block of the blockchain to ensure that the user's location information uploaded by the user terminal is consistent with the user's location information stored in the blockchain. Blockchain is a new application model of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain is essentially a decentralized database. It is a series of data blocks generated using cryptographic methods. Each data block contains a batch of network transaction information and is used to verify its Validity of information (anti-counterfeiting) and generation of the next block. Blockchain can include the underlying platform of the blockchain, the platform product service layer and the application service layer.

Among them, Q=1 indicates that the location verification is passed, and Q=-1 indicates that the location verification is not passed.

When the face verification and location verification pass, a message is sent to obtain the data information corresponding to the request, specifically:

If the face verification is passed but the location verification is not passed, the identity verification will not be passed;

When face verification fails, location verification is not performed, which can save verification processing time;

When the face verification is passed and the location verification is passed, the data information corresponding to the received information acquisition request sent by the user terminal is sent through identity verification.

When actually performing user identity verification, you can also perform location verification on the user's location information in the identity verification information uploaded by the user terminal, and when the location verification fails, face verification is not performed to save verification processing time.

The information acquisition method provided by the present invention can achieve dual security guarantees of user identity and user environment through dual identity verification of face verification and location verification, and can ensure that users perform operations such as payment in a safe environment, avoiding some unscrupulous users. The terminal, based on image processing means, causes the user's property loss through user identity verification.

The information acquisition system provided by the present invention is described below. The information acquisition system described below and the information acquisition method described above can be referenced correspondingly.

Figure 5 is a schematic structural diagram of the information acquisition system provided by the present invention. As shown in Figure 5, it includes:

The first training module 510, the second training module 511, the identity verification module 512 and the information acquisition module 513;

The first training module 510 is used to pre-train the convolutional neural network using the first training sample to obtain the image recognition model;

The second training module 511 is used to optimize and train the image recognition model using the second training sample to obtain the target image recognition model;

The identity verification module 512 is used to perform face verification on the received identity verification information uploaded by the user terminal and perform location verification on the identity verification information based on the target image recognition model;

The information acquisition module 513 is used to send the data information corresponding to the received information acquisition request sent by the user terminal to the user terminal when both the face verification and the location verification are passed;

Wherein, the first training sample is obtained by labeling each environment image according to the first position information corresponding to each environment image in the plurality of acquired environment images;

The second training sample is determined based on the target image obtained by superimposing the face image and the environment image in the first training sample;

The identity verification information includes the user's real-time environment image including the user's face image and the user's location information.

The information acquisition system provided by the present invention can achieve dual security guarantees of user identity and user environment through dual identity verification of face verification and location verification, and avoids the possibility of some unscrupulous user terminals to verify the user identity based on image processing means. Causing property damage to users.

Further, in one embodiment, the first training module 510 can also be specifically used for:

Input the first training sample to the convolutional neural network for pre-training, and perform the hyperparameters of the convolutional neural network based on the comparison results between the second position information and the first position information of each environmental image output by the convolutional neural network. Adjustment;

Based on the adjusted convolutional neural network, the image recognition model is determined.

The information acquisition system provided by the present invention uses the first training sample to train the convolutional neural network to obtain the image recognition model, which lays the foundation for subsequent acquisition of the target recognition model based on the image recognition network and ultimately the verification of the user's location.

Further, in one embodiment, the second training module 511 can also be specifically used for:

The second training sample is input to the image recognition model for optimization training, and based on the comparison result between the third position information and the first position information of the target image output by the image recognition model, the back propagation algorithm and the stochastic gradient algorithm are used to optimize the image recognition model Optimize to obtain the target image recognition model.

The information acquisition system provided by the present invention uses the second training sample to train the image recognition model, which lays the foundation for subsequent determination of the target recognition model based on the trained image recognition network, and ultimately the verification of the user's location.

Further, in one embodiment, the identity verification module 512 can also be specifically used to:

After receiving the information acquisition request sent by the user terminal, identity verification information collection information is sent to the user terminal, so that the user terminal collects identity verification information based on the identity verification information collection information.

The information acquisition system provided by the present invention realizes the dual face verification and location verification of the user by collecting the user's real-time environment image including the user's face image and the user's location information included in the identity verification information uploaded by the user terminal. Identity verification improves the security of verification.

Further, in one embodiment, the identity verification module 512 can also be specifically used to:

extracting a first facial feature of an environment image including a user's facial image;

Calculate feature vectors between the first facial feature and the second facial feature when the user is registered in the identity verification information to obtain the distance between the first facial feature and the second facial feature;

Complete face verification of identity verification information based on distance;

When the face verification is passed, the identity verification information is location verified based on the target image recognition model.

The information acquisition system provided by the present invention realizes environmental image verification based on location information by predicting the location information corresponding to the environment image and comparing the predicted location information with the location information collected by the user, thereby improving the security of the verification.

Further, in one embodiment, the identity verification module 512 can also be specifically used to:

The environment image including the user's face image is input to the target image recognition model, and the location verification of the identity verification information is completed based on the comparison result between the fourth location information output by the target image recognition model and the user's location information.

The information acquisition system provided by the present invention can achieve dual security guarantees of user identity and user environment through dual identity verification of face verification and location verification, and can ensure that users perform operations such as payment in a safe environment, avoiding some unscrupulous users. The terminal, based on image processing means, causes the user's property loss through user identity verification.

Figure 6 is a schematic diagram of the physical structure of an electronic device provided by the present invention. As shown in Figure 6, the electronic device may include: a processor (processor) 610, a communication interface (communication interface) 611, a memory (memory) 612 and a bus. (bus) 613, in which the processor 610, the communication interface 611, and the memory 612 complete communication with each other through the bus 613. Processor 610 can call logic instructions in memory 612 to perform the following methods:

Pre-train the convolutional neural network using the first training sample to obtain the image recognition model;

Use the second training sample to optimize and train the image recognition model to obtain the target image recognition model;

Perform face verification on the received identity verification information uploaded by the user terminal and perform location verification on the identity verification information based on the target image recognition model;

When both face verification and location verification pass, the data information corresponding to the received information acquisition request sent by the user terminal is sent to the user terminal;

Wherein, the first training sample is obtained by labeling each environment image according to the first position information corresponding to each environment image in the plurality of acquired environment images;

The second training sample is determined based on the target image obtained by superimposing the face image and the environment image in the first training sample;

The identity verification information includes the user's real-time environment image including the user's face image and the user's location information.

In addition, the above-mentioned logical instructions in the memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer power panel (which can be a personal computer, a server, or a network power panel, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .

Further, the present invention discloses a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer , the computer can execute the information acquisition method provided by each of the above method embodiments, including, for example:

Pre-train the convolutional neural network using the first training sample to obtain the image recognition model;

Use the second training sample to optimize and train the image recognition model to obtain the target image recognition model;

Perform face verification on the received identity verification information uploaded by the user terminal and perform location verification on the identity verification information based on the target image recognition model;

When both face verification and location verification pass, the data information corresponding to the received information acquisition request sent by the user terminal is sent to the user terminal;

Wherein, the first training sample is obtained by labeling each environment image according to the first position information corresponding to each environment image in the plurality of acquired environment images;

The second training sample is determined based on the target image obtained by superimposing the face image and the environment image in the first training sample;

The identity verification information includes the user's real-time environment image including the user's face image and the user's location information.

On the other hand, the present invention also provides a processor-readable storage medium. The processor-readable storage medium stores a computer program. The computer program is used to cause the processor to execute the methods provided in the above embodiments. Examples include

Pre-train the convolutional neural network using the first training sample to obtain the image recognition model;

Use the second training sample to optimize and train the image recognition model to obtain the target image recognition model;

Perform face verification on the received identity verification information uploaded by the user terminal and perform location verification on the identity verification information based on the target image recognition model;

When both face verification and location verification pass, the data information corresponding to the received information acquisition request sent by the user terminal is sent to the user terminal;

Wherein, the first training sample is obtained by labeling each environment image according to the first position information corresponding to each environment image in the plurality of acquired environment images;

The second training sample is determined based on the target image obtained by superimposing the face image and the environment image in the first training sample;

The identity verification information includes the user's real-time environment image including the user's face image and the user's location information.

The system embodiments described above are only illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in One location, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.

Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the part of the above technical solution that essentially contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., including a number of instructions to cause a computer power panel (which can be a personal computer, a server, or a network power panel, etc.) to execute the methods described in various embodiments or certain parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still be used Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent substitutions are made to some of the technical features; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. An information acquisition method, characterized by comprising:

pre-training the convolutional neural network by using a first training sample to obtain an image recognition model;

performing optimization training on the image recognition model by using a second training sample to obtain a target image recognition model;

carrying out face verification on the received identity verification information uploaded by the user terminal, and carrying out position verification on the identity verification information based on the target image recognition model;

under the condition that the face verification and the position verification are both passed, the received data information corresponding to the information acquisition request sent by the user terminal is sent to the user terminal;

The first training sample is obtained by marking each environment image according to first position information corresponding to each environment image in a plurality of acquired environment images;

the second training sample is determined according to a target image obtained by overlapping the face image and the environment image in the first training sample;

the identity verification information comprises an environment image including a face image of the user and position information of the user, wherein the environment image is shot by the user in real time.

2. The method of claim 1, wherein the pre-training the convolutional neural network using the first training sample to obtain the image recognition model comprises:

inputting the first training sample into the convolutional neural network for pre-training, and adjusting the super-parameters of the convolutional neural network according to the comparison result of the second position information and the first position information of each environment image output by the convolutional neural network;

and determining the image recognition model according to the adjusted convolutional neural network.

3. The information acquisition method according to claim 1, wherein the optimally training the image recognition model using the second training sample to acquire a target image recognition model includes:

And inputting the second training sample into the image recognition model for optimization training, and optimizing the image recognition model by adopting a back propagation algorithm and a random gradient algorithm according to a comparison result of the third position information of the target image and the first position information output by the image recognition model so as to acquire the target image recognition model.

4. The information acquisition method according to claim 1, wherein the authentication information processing position uploaded by the user terminal is determined by:

after receiving the information acquisition request sent by the user terminal, sending authentication information acquisition information to the user terminal, so that the user terminal acquires the authentication information according to the received authentication information acquisition information.

5. The method of claim 1, wherein the performing face verification on the received authentication information uploaded by the user terminal and performing location verification on the authentication information based on the target image recognition model includes:

extracting first face features of the environment image comprising the face image of the user;

Performing feature vector calculation on the first face feature and a second face feature when a user registers in the identity verification information to obtain a distance between the first face feature and the second face feature;

according to the distance, finishing face verification of the identity verification information;

and under the condition that the face verification is passed, carrying out position verification on the identity verification information based on the target image recognition model.

6. The information acquisition method according to claim 5, wherein the performing location verification of the authentication information based on the target image recognition model includes:

and inputting the environment image comprising the face image of the user into the target image recognition model, and completing the position verification of the identity verification information according to the comparison result of the fourth position information output by the target image recognition model and the position information of the user.

7. An information acquisition system, comprising: the system comprises a first training module, a second training module, an identity verification module and an information acquisition module;

the first training module is used for pre-training the convolutional neural network by using a first training sample so as to obtain an image recognition model;

The second training module is used for carrying out optimization training on the image recognition model by using a second training sample so as to obtain a target image recognition model;

the identity verification module is used for carrying out face verification on the received identity verification information uploaded by the user terminal and carrying out position verification on the identity verification information based on the target image recognition model;

the information acquisition module is used for transmitting the received data information corresponding to the information acquisition request transmitted by the user terminal to the user terminal under the condition that the face verification and the position verification are both passed;

the first training sample is obtained by marking each environment image according to first position information corresponding to each environment image in a plurality of acquired environment images;

the second training sample is determined according to a target image obtained by overlapping the face image and the environment image in the first training sample;

the identity verification information comprises an environment image including a face image of the user and position information of the user, wherein the environment image is shot by the user in real time.

8. An electronic device comprising a processor and a memory storing a computer program, characterized in that the processor implements the information acquisition method of any one of claims 1 to 6 when executing the computer program.

9. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program for causing the processor to execute the information acquisition method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the information acquisition method according to any one of claims 1 to 6.